PUBH8485

This course delves into advanced causal inference methods, emphasizing both the theoretical underpinnings and practical applications in public health and biostatistics. Covering a range of topics from randomized experiments to observational studies and beyond, students will engage with the latest methodologies and debates within the field. Through lectures, discussions, and hands-on analysis, students will gain the skills needed to design robust causal studies, analyze complex datasets, and contribute to the cutting edge of causal inference research.

This course uses the R statistical software language, a freely available statistical software package, to implement the methods covered in the course.

This course requires a background knowledge in regression techniques (e.g. linear, logistic regression, among others) at the level of PubH 7405-7406 and additionally requires a solid understanding of statistical theory at the level of Stat 8101-8102. In addition, a familiarity with R for data analysis.